A Review of Data Science and Big Data Computing

Big Data Science Needs Big Data Middleware

There has been a “Cambrian explosion” of big data systems proposed and evaluated in the last eight years, but relatively little understanding of how these systems or the ideas they represent compare and complement one another. In enterprise and science situations, “one size is unlikely to fit all”: we see analytics teams running multiple systems simultaneously. However, the highest level of abs...

Big Data, Data Science, and Civil Rights

Advances in data analytics bring with them civil rights implications. Data-driven and algorithmic decision making increasingly determine how businesses target advertisements to consumers, how police departments monitor individuals or groups, how banks decide who gets a loan and who does not, how employers hire, how colleges and universities make admissions and financial aid decisions, and much ...

Data science, big data and granular mining

Data science, big data and granular mining

With the evolution of various modern technologies, huge amount f data is being constantly generated and collected around us. We re in the midst of what is popularly called information revolution nd are living in a so-called world of knowledge. Intentionally and/or ccidentally, generation of these data is inevitable. As a result, large ata, broadly characterised by three Vs – large volume, veloc...

Big Data and Fog Computing

Fog computing serves as a computing layer that sits between the edge devices and the cloud in the network topology. They have more compute capacity than the edge but much less so than cloud data centers. They typically have high uptime and always-on Internet connectivity. Applications that make use of the fog can avoid the network performance limitation of cloud computing while being less resou...